The role of sensors such as cameras or LiDAR (Light Detection and Ranging) is crucial for
the environmental awareness of self-driving cars. However, the data collected from these …

Abstract Domain-adaptive semantic segmentation aims to transfer knowledge from a labeled
source domain to an unlabeled target domain. However, existing methods primarily focus on …

Autonomous driving technology has advanced significantly with deep learning, but noise
and attacks threaten its real-world deployment. While research has revealed vulnerabilities …

The growing amount of data demands methods that can gradually learn from new samples.
However, it is not trivial to continually train a network. Retraining a network with new data …

Object detection plays a crucial role in various cutting-edge applications, such as
autonomous vehicles and advanced robotics systems, primarily relying on conventional …

Radar point clouds will experience variations in density, which may cause incorrect alerts
during clustering. In turn, it will diminish the precision of the decision-level fusion method. To …

With the rapid recent development, deep learning based object detection techniques have
been applied to various real-world software systems, especially in safety-critical applications …

Camera-based object detection is integral to advanced driver assistance systems (ADAS)
and autonomous vehicle research, and RGB cameras remain indispensable for their spatial …

Unmanned systems encounter challenging weather conditions during obstacle removal
tasks. Researching stable, real-time, and accurate environmental perception methods under …